Aspect-based sentiment analysis for online reviews with hybrid attention networks

Aspect-based sentiment analysis has received considerable attention in recent years because it can provide more detailed and specific user opinion information. Most existing methods based on recurrent neural networks usually suffer from two drawbacks: information loss for long sequences and a high time consumption. To address such issues, a hybrid attention model is proposed for aspect-based sentiment analysis in this paper, which utilizes only attention mechanisms rather than recurrent or convolutional structures. In this model, a self-attention mechanism and an aspect-attention mechanism are designed for generating the semantic representation at the word and sentence levels respectively. Two auxiliary features of word location and part-of-speech are also explored for the proposed models to enhance the semantic representation of sentences. A series of experiments are conducted on three benchmark datasets for aspect-based sentiment analysis. Experimental results demonstrate the advantage of the proposed models for both efficiency and execution effectiveness.